Device-to-device (D2D) communication is a potential technology to improve capacity and energy efficiency of the current wireless communication systems. This work is focused on the application of D2D communication underlaying LTE network for distributed automation (DA) in the smart grid (SG) context. The communication aspects of SG and DA are introduced, and considering the strict reliability an...

We consider the closely related problems of bandit convex optimization with two-point feedback, and zero-order stochastic convex optimization with two function evaluations per round. We provide a simple algorithm and analysis which is optimal for convex Lipschitz functions. This improves on Duchi et al. (2015), which only provides an optimal result for smooth functions; Moreover, the algorithm ...

Convex optimization is a special class of optimization problems, that includes many problems of interest such as least squares and linear programming problems. Convex optimization problems are considered especially important because several efficient algorithms exist for solving them; as a result, many machine learning problems have been modeled as convex optimization. In a typical convex optim...

We propose an optimization method for minimizing the finite sums of smooth convex functions. Our method incorporates an accelerated gradient descent (AGD) and a stochastic variance reduction gradient (SVRG) in a mini-batch setting. Unlike SVRG, our method can be directly applied to non-strongly and strongly convex problems. We show that our method achieves a lower overall complexity than the re...

Most learning methods with rank or sparsity constraints use convex relaxations, which lead to optimization with the nuclear norm or the `1-norm. However, several important learning applications cannot benefit from this approach as they feature these convex norms as constraints in addition to the non-convex rank and sparsity constraints. In this setting, we derive efficient sparse projections on...

This paper studies the nonlinear optimization problems subject to bipolar max-min fuzzy relation equation constraints. The feasible solution set of the problems is non-convex, in a general case. Therefore, conventional nonlinear optimization methods cannot be ideal for resolution of such problems. Hence, a Genetic Algorithm (GA) is proposed to find their optimal solution. This algorithm uses th...

This paper concerns sub-channel allocation in multi-user wireless networks with a view to increasing the network throughput. It is assumed there are some sub-channels to be equally divided among active links, such that the total sum rate increases, where it is assumed each link is subject to a maximum transmit power constraint. This problem is found to be a non-convex optimization problem and i...

I. Introduction: Optimization of non-convex (multi-modal) functions is the subject matter of research in global optimization. During the 1970's or before only little work was done in this field, but in the 1980's it attracted the attention of many researchers. Since then, a number of methods have been proposed to find the global optima of non-convex (multi-modal) problems of combinatorial as we...

the problem of finding the minimum cost multi-commodity flow in an undirected and completenetwork is studied when the link costs are piecewise linear and convex. the arc-path model and overflowmodel are presented to formulate the problem. the results suggest that the new overflow model outperformsthe classical arc-path model for this problem. the classical revised simplex, frank and wolf and a ...

This paper presents an application of canonical duality theory to the solution of contact problems with Coulomb friction. The contact problem is formulated as a quasi-variational inequality whose solution is found by solving its Karush– Kuhn–Tucker system of equations. The complementarity conditions are reformulated by using the Fischer–Burmeister complementarity function, obtaining a non-conve...